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Innovations in the Statistical Analysis of Twin Studies

Published online by Cambridge University Press:  01 August 2014

J.L. Hopper*
Affiliation:
Faculty of Medicine Epidemiology Unit, University of Melbourne, Carlton, Victoria, Australia
P.L. Derrick
Affiliation:
Faculty of Medicine Epidemiology Unit, University of Melbourne, Carlton, Victoria, Australia
C.A. Clifford
Affiliation:
Faculty of Medicine Epidemiology Unit, University of Melbourne, Carlton, Victoria, Australia
*
The University of Melbourne, Faculty of Medicine Epidemiology Unit, 151 Barry Street, Carlton, Victoria 3053, Australia

Abstract

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Advances in computer technology have made possible a greater sophistication in the statistical analysis of pedigree data, however this is not necessarily manifest by fitting more comprehensive causative models. Planned twin and family studies measure numerous explanatory variables, including perhaps genetic and DNA marker information status on all pedigree members, and the cohabitation of all pairs of individuals. A statistical analysis should examine the contribution of these measured factors on individual means, and in explaining the variation and covariation between individuals, concurrently with the postulated effect of unmeasured factors such as polygenes. We present two models that meet this requirement: the Multivariate Normal Model for Pedigree Analysis for quantitative traits, and a Log-Linear Model for Binary Pedigree Data. For both models, important issues are examination of fit, detection of outlier pedigrees and outlier individuals, and critical examination of the model assumptions. Procedures for fulfilling these needs and examples of modelling are discussed.

Type
Research Article
Copyright
Copyright © The International Society for Twin Studies 1987

References

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